5,881 research outputs found
Proving uniformity and independence by self-composition and coupling
Proof by coupling is a classical proof technique for establishing
probabilistic properties of two probabilistic processes, like stochastic
dominance and rapid mixing of Markov chains. More recently, couplings have been
investigated as a useful abstraction for formal reasoning about relational
properties of probabilistic programs, in particular for modeling
reduction-based cryptographic proofs and for verifying differential privacy. In
this paper, we demonstrate that probabilistic couplings can be used for
verifying non-relational probabilistic properties. Specifically, we show that
the program logic pRHL---whose proofs are formal versions of proofs by
coupling---can be used for formalizing uniformity and probabilistic
independence. We formally verify our main examples using the EasyCrypt proof
assistant
Advanced Probabilistic Couplings for Differential Privacy
Differential privacy is a promising formal approach to data privacy, which
provides a quantitative bound on the privacy cost of an algorithm that operates
on sensitive information. Several tools have been developed for the formal
verification of differentially private algorithms, including program logics and
type systems. However, these tools do not capture fundamental techniques that
have emerged in recent years, and cannot be used for reasoning about
cutting-edge differentially private algorithms. Existing techniques fail to
handle three broad classes of algorithms: 1) algorithms where privacy depends
accuracy guarantees, 2) algorithms that are analyzed with the advanced
composition theorem, which shows slower growth in the privacy cost, 3)
algorithms that interactively accept adaptive inputs.
We address these limitations with a new formalism extending apRHL, a
relational program logic that has been used for proving differential privacy of
non-interactive algorithms, and incorporating aHL, a (non-relational) program
logic for accuracy properties. We illustrate our approach through a single
running example, which exemplifies the three classes of algorithms and explores
new variants of the Sparse Vector technique, a well-studied algorithm from the
privacy literature. We implement our logic in EasyCrypt, and formally verify
privacy. We also introduce a novel coupling technique called \emph{optimal
subset coupling} that may be of independent interest
Non-GAAP earnings and stock price crash risk
Lee Kong Chian Fellowship, SM
Disentangling Boosted Higgs Boson Production Modes with Machine Learning
Higgs Bosons produced via gluon-gluon fusion (ggF) with large transverse
momentum () are sensitive probes of physics beyond the Standard Model.
However, high Higgs Boson production is contaminated by a diversity of
production modes other than ggF: vector boson fusion, production of a Higgs
boson in association with a vector boson, and production of a Higgs boson with
a top-quark pair. Combining jet substructure and event information with modern
machine learning, we demonstrate the ability to focus on particular production
modes. These tools hold great discovery potential for boosted Higgs bosons
produced via ggF and may also provide additional information about the Higgs
Boson sector of the Standard Model in extreme phase space regions for other
production modes as well.Comment: 17 pages, 9 figure
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